ArticleComputers in biology and medicine2021
Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Methods of Assessing Postoperative Pain in Infants (0-12 Months): A Scoping Review.Paediatric & neonatal pain · 2026Review
- MSAFNet: A multi-scale attention fusion network for automated neonatal pain facial expression recognition with clinical validation.Scientific reports · 2026Article
- Is this neonate feeling pain? Leveraging clinical knowledge towards high-precision Large Language Model-based neonatal pain assessment.Pediatric research · 2026Article
- Beyond the face: advancing multimodal AI for neonatal pain assessment.Pediatric research · 2026Article
- AI in pediatric surgery: a narrative review.Translational pediatrics · 2026Review
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Pharmacological therapy of neonatal analgosedation: current status, dilemmas, and perspectives.Frontiers in pediatrics · 2026Review
- Application of Intelligent Neonatal Pain Assessment Tools Based on Multimodal Data Fusion: A Systematic Review.Journal of pain research · 2026Review
- Touchless monitoring of neonatal activity: a multi-center study.Pediatric research · 2026Article
- Pain assessment and determination methods with wearable sensors: a scoping review.Medical & biological engineering & computing · 2026Article
- Predictive machine learning for postoperative pain using biosignals: a retrospective observational study.BMC medical informatics and decision making · 2025Observational
- Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants.Pain reports · 2025Article
- Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.Bioengineering (Basel, Switzerland) · 2025Review
- TAME Pain data release: using audio signals to characterize pain.Scientific data · 2025Article
- Review
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- Review
- Explainable AI (XAI) for Neonatal Pain Assessment via Influence Function Modification.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The current practice for assessing neonatal postoperative pain relies on bedside caregivers. This practice is subjective, inconsistent, slow, and discontinuous. To develop a reliable medical interpretation, several automated approaches have been proposed to enhance the current practice. These approaches are unimodal and focus mainly on assessing neonatal procedural (acute) pain. As pain is a multimodal emotion that is often expressed through multiple modalities, the multimodal assessment of pain is necessary especially in case of postoperative (acute prolonged) pain. Additionally, spatio-temporal analysis is more stable over time and has been proven to be highly effective at minimizing misclassification errors. In this paper, we present a novel multimodal spatio-temporal approach that integrates visual and vocal signals and uses them for assessing neonatal postoperative pain. We conduct comprehensive experiments to investigate the effectiveness of the proposed approach. We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration. The experimental results, on a real-world dataset, show that the proposed multimodal spatio-temporal approach achieves the highest AUC (0.87) and accuracy (79%), which are on average 6.67% and 6.33% higher than unimodal approaches. The results also show that the integration of temporal information markedly improves the performance as compared to the non-temporal approach as it captures changes in the pain dynamic. These results demonstrate that the proposed approach can be used as a viable alternative to manual assessment, which would tread a path toward fully automated pain monitoring in clinical settings, point-of-care testing, and homes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.